Inflammatory diseases (ID) are incurable, progressive diseases. Literature evidence cites increasing incidence of these diseases worldwide. When treatments with chemical immunosuppressive agents fail, patients are often treated with monoclonal antibodies (MAbs). However, MAb failure rates are generally high, with approximately half the patients being discontinued within 4 years, necessitating switching to another MAb. One potential cause of treatment failure is subtherapeutic exposure. Several studies demonstrated associations between trough MAb concentrations and clinical response, supporting the notion that improving drug exposure may result in improved outcomes. MAbs exhibit complex and highly variable pharmacokinetics in ID patients with numerous factors affecting clearance. Bayesian-guided dosing with dashboard systems is a new tool being investigated in the treatment of ID to reduce variability in exposure. Simulations suggest dashboards will be effective at maintaining patients at target troughs. However, when patients are dosed using doses or intervals outside those listed in prescribing information, there is concern that patients may have drug exposures beyond or below the ranges found to be safe and efficacious. This manuscript reviews the rationale behind dashboard development, evaluations of expected performance, and a simulated assessment of MAb exposure with dashboard-based dosing versus dosing based on the prescribing information. We introduce the concept of pharmacologic equivalence-if patients are dosed based on individual pharmacokinetics, the resulting exposure is consistent with exposures achieved using labeled dosing. We further show that dashboard-based dosing results in observed exposures that are generally contained within the range of exposures achieved with labeled dosing.
Keywords: Bayes; IBD; dashboard; individualized dosing; monoclonal antibodies.